The A2SF post-season survey was disseminated via Instagram, email, _________________(?). This led to bot responses, despite CAPTCHA use. The following code attempts to identify and filter those responses out prior to completing more in-depth analysis, but those data can be easily re-incorporated as needed.
Remove non-US auto-collected countries and manual zip code entries that are not 5 characters
Create a new column that determines initial Bot/Not Bot status by comparing auto-collected Country with manual-collected Zip Code
| Respondent ID | State | Country Code | Bot |
|---|---|---|---|
| 304885124 | Alabama | US | Not Bot |
| 304885234 | Arizona | US | Not Bot |
| 304882108 | California | US | Not Bot |
| 304882174 | California | US | Not Bot |
| 304883063 | California | US | Not Bot |
| 304883546 | California | US | Not Bot |
Checked for unusually short-duration surveys based on Start and
End Date data. 0 Identified.
Created a new file after manually reading responses. Flagged previous “Not Bot”s as “Maybe”. Also filtered to leave only “Not Bot” remaining.
I collapsed all zip code counts of 1 into a single “Other” categorization.
I had to manually geocode some components of latitude and longitude based on zip code because the geocode() function is not perfect. This allows me to map the zip code regions.
“How many Superior Township and Ypsi/Township, Ann Arbor, Washtenaw County, outside Washtenaw, out of state?” * Cannot determine townships because zip codes don’t generally match up with city or town boundaries. The best I can do is match zip code information with the “Major city” in each region. Can definitely do counties and out of state though.
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